You can query local JSON and NDJSON files with SQL without writing a custom parser. DuckDB’s JSON table functions read files directly in a FROM clause; then you can select fields, filter and aggregate rows, and write results using ordinary SQL. The first step is identifying the file layout: a top-level JSON array and newline-delimited JSON (NDJSON) need different format handling.
First identify the JSON file layout
A JSON extension alone does not tell you how records are arranged. Two common layouts are:
- Top-level array: one JSON document containing an array of objects, such as
[{"id":1},{"id":2}]. - NDJSON: one independent JSON value per line, such as
{"id":1}followed by{"id":2}on the next line. Files often use extensions such as.jsonlor.ndjson, but the contents determine the format.
DuckDB documents format = 'array' for a top-level array of records and format = 'newline_delimited' when each line is a record. Its JSON loading reference covers current options and defaults, which can vary by installed version.
Query a local file with DuckDB
DuckDB exposes JSON readers as table functions, so a file can appear directly in the SQL FROM clause. For a JSON file whose layout and columns can be inferred:
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SELECT *
FROM read_json('events.json')
LIMIT 10;For a file where each line is an independent event, use the NDJSON reader:
SELECT event_type, count(*) AS events
FROM read_ndjson('events.jsonl')
GROUP BY event_type
ORDER BY events DESC;The result is a relational query: fields become columns, records become rows, and SQL handles filtering, grouping, joins, and aggregation. DuckDB also documents reading a list of files or a glob pattern; consult the loading reference for the syntax supported by your version.
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Control schema inference when files vary
Automatic detection is a useful starting point, not a guarantee that every file will produce the schema you want. Files may omit keys, represent a field with different types, or contain nested objects and arrays. Inspect the inferred columns and types first, then declare the projection and types explicitly if needed.
SELECT id, event_type
FROM read_json(
'events.jsonl',
format = 'newline_delimited',
columns = {id: 'UBIGINT', event_type: 'VARCHAR'}
);This explicitly selects two columns and assigns their SQL types. The DuckDB loading documentation describes schema-detection controls including sample_size, maximum_depth, and union_by_name. These options help tune inference or combine schemas across multiple files; check the current reference for their exact behavior and option spelling in your installed release. When a record lacks a key represented in the resulting schema, that field can be NULL.
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Choose a strategy for nested objects and arrays
The right approach depends on whether you need a few scalar values, recurring analysis of a nested structure, or a row for every element in an array or object. DuckDB’s JSON functions support extraction, transformation, and traversal.
Extract a few nested values
Use a JSON path to retrieve an individual value. For example, this extracts a nested customer name as a string:
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SELECT json_extract_string(payload, '$.customer.name') AS customer_name
FROM events;Transform JSON into nested SQL types
For repeated analysis of the same nested shape, DuckDB’s json_transform (also available as from_json) can convert JSON into nested SQL STRUCT and LIST values. This makes the structure explicit for later SQL operations rather than repeatedly navigating raw JSON.
Expand objects or arrays into rows
Use json_each to turn the members of an object or elements of an array into rows. A table function in the FROM clause can refer to an earlier table item; this is lateral behavior, so each event’s items are expanded in the context of that event.
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SELECT e.id, item.key, item.value
FROM events AS e,
json_each(e.payload, '$.items') AS item;For a recursive, depth-first view of a JSON structure, DuckDB also provides json_tree. See the JSON functions reference for the available columns and function details.
Watch the indexing difference
When indexing into JSON in DuckDB, the first array element is at index 0. DuckDB’s SQL LIST and ARRAY types instead use index 1. Keep the value’s type in mind when moving between JSON extraction and transformed SQL values; the same numeric index does not mean the same position in both.
DuckDB’s JSON overview states: “DuckDB supports SQL functions that are useful for reading values from existing JSON and creating new JSON data.”
When PostgreSQL or BigQuery is a better fit
These are alternatives for data already in their respective systems, not identical substitutes for DuckDB’s direct local-file workflow.
| Engine | When it fits | JSON workflow |
|---|---|---|
| DuckDB | You want to query local JSON or NDJSON files directly. | JSON table functions read files in SQL; inference and explicit schema controls are available. Documentation. |
| PostgreSQL 17 | The JSON records are already available to a PostgreSQL query. | JSON_TABLE uses a JSON path row pattern and a COLUMNS clause to project JSON values into relational columns. It is documented in the PostgreSQL 17 JSON functions reference. |
| BigQuery | You want to load or query the data in Google Cloud’s managed warehouse. | BigQuery supports a native JSON type and loading newline-delimited JSON with the NEWLINE_DELIMITED_JSON source format. Its JSON data documentation, consulted October 4, 2026, states a 500-level nesting limit and notes that JSON columns cannot be used for partitioning or clustering. These service constraints can change. |
For BigQuery extraction, current documentation includes JSON_QUERY and JSON_VALUE. Some older JSON_EXTRACT* functions are marked deprecated, so prefer the current BigQuery JSON functions reference when writing new queries.
Quick Recap
A practical way to choose
- Choose by data location: start with DuckDB for local files; use PostgreSQL’s JSON functions for data already queried there; use BigQuery when a managed warehouse workflow is appropriate.
- Choose by file shape: distinguish NDJSON from a top-level array, and explicitly set the format when inference does not match the file.
- Choose by schema stability: let inference help with exploration, then specify columns or schema-union controls when file shapes vary.
- Choose by nested operation: extract scalar fields for a few values, transform recurring structures into nested SQL types, or expand variable arrays and objects into rows.
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